Symmetry-based computational search for novel binary and ternary 2D materials
Hai-Chen Wang (1), Jonathan Schmidt (1), Miguel A.L. Marques (1),, Ludger Wirtz (2), Aldo H. Romero (3) ((1) Martin-Luther-Universit\"at, Halle-Wittenberg, (2) University of Luxembourg, (3) West Virginia University)

TL;DR
This paper introduces a symmetry-based, exhaustive computational approach combining neural-network force-fields and density-functional theory to discover a vast array of novel 2D materials across the periodic table, including many previously unknown compounds.
Contribution
The study presents a comprehensive method for discovering 2D materials using symmetry, combinatorial construction, machine learning, and quantum calculations, revealing extensive new material possibilities.
Findings
Discovered around 6500 new 2D compounds not in existing databases.
Identified structures with diverse tilings and polymorphisms.
Found many compounds with low energy above the stability threshold.
Abstract
We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form AB or ABC. Among the found structures we find examples that can be built by decorating nearly all…
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Taxonomy
TopicsMachine Learning in Materials Science · 2D Materials and Applications · Boron and Carbon Nanomaterials Research
